Upscaler.load_model: don't return None, just use exceptions

modules/esrgan_model.py

@@ -1,4 +1,4 @@
-import os
+import sys
 
 import numpy as np
 import torch
@@ -6,9 +6,8 @@ from PIL import Image
 
 import modules.esrgan_model_arch as arch
 from modules import modelloader, images, devices
-from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import opts
-
+from modules.upscaler import Upscaler, UpscalerData
 
 
 def mod2normal(state_dict):
@@ -142,8 +141,10 @@ class UpscalerESRGAN(Upscaler):
             self.scalers.append(scaler_data)
 
     def do_upscale(self, img, selected_model):
-        model = self.load_model(selected_model)
-        if model is None:
+        try:
+            model = self.load_model(selected_model)
+        except Exception as e:
+            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
             return img
         model.to(devices.device_esrgan)
         img = esrgan_upscale(model, img)
@@ -159,9 +160,6 @@ class UpscalerESRGAN(Upscaler):
             )
         else:
             filename = path
-        if not os.path.exists(filename) or filename is None:
-            print(f"Unable to load {self.model_path} from {filename}")
-            return None
 
         state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
 

modules/realesrgan_model.py

@@ -9,7 +9,6 @@ from modules.shared import cmd_opts, opts
 from modules import modelloader, errors
 
 
-
 class UpscalerRealESRGAN(Upscaler):
     def __init__(self, path):
         self.name = "RealESRGAN"
@@ -43,9 +42,10 @@ class UpscalerRealESRGAN(Upscaler):
         if not self.enable:
             return img
 
-        info = self.load_model(path)
-        if not os.path.exists(info.local_data_path):
-            print(f"Unable to load RealESRGAN model: {info.name}")
+        try:
+            info = self.load_model(path)
+        except Exception:
+            errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
         upsampler = RealESRGANer(
@@ -63,20 +63,17 @@ class UpscalerRealESRGAN(Upscaler):
         return image
 
     def load_model(self, path):
-        try:
-            info = next(iter([scaler for scaler in self.scalers if scaler.data_path == path]), None)
-
-            if info is None:
-                print(f"Unable to find model info: {path}")
-                return None
-
-            if info.local_data_path.startswith("http"):
-                info.local_data_path = modelloader.load_file_from_url(info.data_path, model_dir=self.model_download_path)
-
-            return info
-        except Exception:
-            errors.report("Error making Real-ESRGAN models list", exc_info=True)
-        return None
+        for scaler in self.scalers:
+            if scaler.data_path == path:
+                if scaler.local_data_path.startswith("http"):
+                    scaler.local_data_path = modelloader.load_file_from_url(
+                        scaler.data_path,
+                        model_dir=self.model_download_path,
+                    )
+                if not os.path.exists(scaler.local_data_path):
+                    raise FileNotFoundError(f"RealESRGAN data missing: {scaler.local_data_path}")
+                return scaler
+        raise ValueError(f"Unable to find model info: {path}")
 
     def load_models(self, _):
         return get_realesrgan_models(self)